Representing knowledge in a triple store is trivial, yet querying and visualizing the resulting knowledge is difficult and inefficient when the number of triples is large. Needing to understand the data models from each of the contributing processes and how these data models overlap or interact further complicates this problem. Visualization tools for knowledge stored in the Resource Description Framework (RDF) tend to simply enable visualization of the data via a graph. While this does show the available data in a relatively intuitive manner, it simply does not scale. We will automatically identify intelligible, useful concepts that show how entities relate and expose undeclared relationships in the knowledge base. We will develop tools and techniques for concept generation to augment class/concept structures available from ontologies describing the knowledge store. We address the main problem in two steps: (1) feature selection, (2) analytics and visualization. This proposal describes our proposed methodology for extracting features of entities described in an RDF knowledge base, and the application of these features to automatic concept map generation. We propose to develop a scalable manifold learning algorithm for concept extraction that will also enable a broader application of machine learning algorithms to RDF data at scale.